Affordance-Based Robot Control for Tractable Action Selection
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Solution Overview
Problem
Existing robotic control systems face challenges in efficiently determining next actions to perform complex manipulation tasks due to the need to consider the entire physical environment and an unrestricted set of options, leading to high computational costs and limited success.
Innovation Solution
The proposed affordance-based control system processes data characterizing a physical environment using a first set of machine learning models to identify a set of affordable actions, which are then processed by a second set of models to select and execute specific actions, thereby reducing computational complexity and improving task success rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the control system considers the entire physical environment and unrestricted set of options to determine next actions, then the robot can perform complex manipulation tasks, but the computational costs become high and success rate is limited
Solution Approach 1:
The patent segments the control system into two distinct sets of machine learning models: a first set that processes environmental data to identify affordable actions, and a second set that selects and executes specific actions. This segmentation divides the previously monolithic complex control system into manageable modules, reducing overall computational complexity while maintaining the ability to perform complex manipulation tasks.
Solution Approach 2:
The system performs preliminary action by using the first set of machine learning models to pre-identify a set of affordable actions before the second set selects the specific action to execute. This preliminary filtering of action options reduces the computational burden on the action selection stage, allowing the system to handle complex tasks efficiently.
2Reliability
If the control system processes the entire physical environment data, then it can determine appropriate actions, but the computational time and resources increase
Solution Approach 1:
The patent extracts only the relevant information from the entire physical environment data by using the first set of machine learning models to identify a filtered set of affordable actions. This extraction process removes unnecessary computational overhead while retaining the essential information needed for accurate action selection, thereby reducing computational time without sacrificing reliability.
3Adaptability or versatility
If the system uses an unrestricted set of action options, then it can handle diverse tasks, but the computational burden increases and learning efficiency decreases
Solution Approach 1:
The system dynamically adapts the set of affordable actions based on the specific task requirements and environmental context. The first set of machine learning models generates a task-specific subset of affordable actions from the unrestricted action space, allowing the system to maintain versatility for diverse tasks while optimizing learning efficiency by focusing computational resources on relevant actions only.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for processing data via a set of machine learning models to cause a device to perform a task. The method generally includes accessing data characterizing a physical environment in which a device is operating. A set of affordable actions is generated based on processing the data via a first set of machine learning models. A selected action to be performed in the physical environment is generated via a second set of machine learning models based on the set of affordable actions and a task. The device is then caused to execute the first selected action.